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Emotion Recognition Using Multiple Kernel Learning toward E-learning Applications

Emotion Recognition Using Multiple Kernel Learning toward E-learning Applications Adaptive Educational Hypermedia (AEH) e-learning models aim to personalize educational content and learning resources based on the needs of an individual learner. The Adaptive Hypermedia Architecture (AHA) is a specific implementation of the AEH model that exploits the cognitive characteristics of learner feedback to adapt resources accordingly. However, beside cognitive feedback, the learning realm generally includes both the affective and emotional feedback of the learner, which is often neglected in the design of e-learning models. This article aims to explore the potential of utilizing affect or emotion recognition research in AEH models. The framework is referred to as Multiple Kernel Learning Decision Tree Weighted Kernel Alignment (MKLDT-WFA). The MKLDT-WFA has two merits over classical MKL. First, the WFA component only preserves the relevant kernel weights to reduce redundancy and improve the discrimination for emotion classes. Second, training via the decision tree reduces the misclassification issues associated with the SimpleMKL. The proposed work has been evaluated on different emotion datasets and the results confirm the good performances. Finally, the conceptual Emotion-based E-learning Model (EEM) with the proposed emotion recognition framework is proposed for future work. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) Association for Computing Machinery

Emotion Recognition Using Multiple Kernel Learning toward E-learning Applications

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2018 ACM
ISSN
1551-6857
eISSN
1551-6865
DOI
10.1145/3131287
Publisher site
See Article on Publisher Site

Abstract

Adaptive Educational Hypermedia (AEH) e-learning models aim to personalize educational content and learning resources based on the needs of an individual learner. The Adaptive Hypermedia Architecture (AHA) is a specific implementation of the AEH model that exploits the cognitive characteristics of learner feedback to adapt resources accordingly. However, beside cognitive feedback, the learning realm generally includes both the affective and emotional feedback of the learner, which is often neglected in the design of e-learning models. This article aims to explore the potential of utilizing affect or emotion recognition research in AEH models. The framework is referred to as Multiple Kernel Learning Decision Tree Weighted Kernel Alignment (MKLDT-WFA). The MKLDT-WFA has two merits over classical MKL. First, the WFA component only preserves the relevant kernel weights to reduce redundancy and improve the discrimination for emotion classes. Second, training via the decision tree reduces the misclassification issues associated with the SimpleMKL. The proposed work has been evaluated on different emotion datasets and the results confirm the good performances. Finally, the conceptual Emotion-based E-learning Model (EEM) with the proposed emotion recognition framework is proposed for future work.

Journal

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) – Association for Computing Machinery

Published: Jan 4, 2018

Keywords: E-learning

References

  • Emotion recognition for user centred e-learning
    Akputu, Oryina K.; Adedolapo, Abiodun O.
  • Improved multimodal emotion recognition for better game-based learning
    Bahreini, Kiavash; Nadolski, Rob; Westera, Wim
  • Towards multimodal emotion recognition in e-learning environments
    Bahreini, Kiavash; Nadolski, Rob; Westera, Wim
  • AHA! An open adaptive hypermedia architecture
    De Bra, Paul; Calvi, Licia
  • Developing adaptive educational hypermedia systems: From design models to authoring tools
    Brusilovsky, Peter
  • Multiple kernel learning for visual object recognition: A review
    Bucak, Serhat S.; Jin, Rong; Jain, Anil K.
  • Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression
    Daugman, J. G.
  • Emotion recognition in the wild challenge 2014
    Dhall, Abhinav; Goecke, Roland; Joshi, Jyoti; Sikka, Karan; Gedeon, Tom
  • Gaze tutor: A gaze-reactive intelligent tutoring system
    D’Mello, Sidney; Olney, Andrew; Williams, Claire; Hays, Patrick
  • Facial Action Coding System: A Technique for the Measurement of Facial Movement: Investigator’s Guide 2 Parts
    Ekman, P.; Friesen, W.
  • Improved spatiotemporal local monogenic binary pattern for emotion recognition in the wild
    Huang, Xiaohua; He, Qiuhai; Hong, Xiaopeng; Zhao, Guoying; Pietikainen, Matti
  • Finding Groups in Data: An Introduction to Cluster Analysis
    Kaufman, Leonard; Rousseeuw, Peter J.
  • Recognizing student emotions and attitudes on the basis of utterances in spoken tutoring dialogues with both human and computer tutors
    Litman, Diane J.; Forbes-Riley, Kate
  • The extended Cohn-Kanade dataset (CK+): A complete dataset for action unit and emotion-specified expression
    Lucey, Patrick; Cohn, Jeffrey F.; Kanade, Takeo; Saragih, Jason; Ambadar, Zara; Matthews, Iain
  • On kernel target alignment
    Nello, Cristianini; Kandola, Jaz; Elisseeff, Andre; Shawe-Taylor, John
  • Web-based database for facial expression analysis
    Pantic, M.; Valstar, M.; Rademaker, R.; Maat, L.
  • Voice interactive learning
    Paule-Ruiz, MPuerto; Álvarez-García, Víctor; Pérez-Pérez, J. R.; Riestra-González, M.
  • Softwares and Toolboxes
    Rakotomamonjy, Alain
  • SimpleMKL
    Rakotomamonjy, Alain; Bach, Francis; Canu, Stephane; Grandvalet, Yves
  • How do you know that I don’t understand? A look at the future of intelligent tutoring systems
    Sarrafzadeh, Abdolhossein; Alexander, Samuel; Dadgostar, Farhad; Fan, Chao; Bigdeli, Abbas
  • Multimodal interfaces: Challenges and perspectives
    Sebe, Nicu
  • Emotion recognition based on joint visual and audio cues
    Sebe, N.; Cohen, I.; Gevers, T.; Huang, T. S.
  • Affective e-Learning in residential and pervasive computing environments
    Shen, Liping; Callaghan, Victor; Shen, Ruimin
  • Recognizing human emotional state via SRC in fractional Fourier domain
    Song, Teng; Qi, Lin; Chen, Enqing; Gao, Lei; Zheng, Ning
  • Component-based recognition of faces and facial expressions
    Taheri, Sima; Patel, Vishal M.; Chellappa, Rama
  • Robust real-time face detection
    Viola, Paul; Jones, Michael J.
  • Recognizing human emotional state from audiovisual signals
    Wang, Yongjin; Guan, Ling
  • A local experts organization model with application to face emotion recognition
    Wong, Jia-Jun; Cho, Siu-Yeung